CN113541205B - Cluster learning-based low-carbon CSP system collaborative optimization method and device - Google Patents
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Abstract
The invention discloses a low-carbon CSP system planning and operation collaborative optimization method and device based on cluster learning, which comprises the following steps: performing cluster grouping on CSP units in the system to obtain a plurality of CSP unit groups; constructing output power constraint, climbing constraint, minimum on-line time constraint, minimum off-line time constraint and instantaneous thermal power balance constraint of the CSP unit group by three continuous variables representing the on-line total capacity, the starting total capacity and the stopping total capacity of the CSP unit group, and further constructing a low-carbon CSP system planning and operation collaborative optimization model; acquiring the rated capacity of each unit in the low-carbon CSP system; and acquiring a capacity allocation scheme of each unit group according to the rated capacity of each unit and the constructed low-carbon CSP system planning and operation collaborative optimization model. The variables in the constraints of the CSP unit group are continuous, the CSP unit group is a complete linear optimization model, the complexity of model calculation is reduced, and the CSP unit group is suitable for analyzing the long-term planning problem of a large-scale power system.
Description
Technical Field
The invention relates to the technical field of power system planning, in particular to a low-carbon CSP system collaborative optimization method and device based on cluster learning.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In order to further improve the permeability of renewable energy in the power system, solar thermal power (CSP) units are receiving more and more attention from people, because they can not only generate power by using renewable energy, but also effectively improve the operational flexibility of the system. In the invention, a power system comprising a large number of renewable energy power generating sets such as CSP sets is called a low-carbon CSP system. When the annual operation cost analysis and calculation of the long-term planning problem are carried out on the CSP system, the model established about the CSP unit is a mixed integer linear programming model, and when the mixed integer linear programming model is solved and calculated, the calculation speed is low. Currently, some research focuses on reducing the computational complexity of a planning model for analyzing a long-term planning problem, for example, reducing the computational burden of the long-term planning problem by methods such as reducing complex scenes and simplifying constraint conditions; or from the modeling perspective, clustering technology is adopted to group the same or similar units in the unit combination formula to reduce the calculation complexity, but the methods do not change the mixed integer property of the model, so that the calculation speed is still slow when the model is solved.
Disclosure of Invention
The invention provides a low-carbon CSP system collaborative optimization method and a device based on cluster learning to solve the problems, firstly, CSP units in the system are divided into groups, then, three continuous variables representing the on-line total capacity, the start total capacity and the shut-down total capacity of the CSP unit groups are used for constructing each constraint of the CSP unit groups, and further, a low-carbon CSP system planning and operation collaborative optimization model is constructed, because each constraint of the constructed CSP unit groups does not have a binary variable representing the on-off state of a single unit, each constraint of the constructed CSP unit groups is a complete linear optimization model, the calculation complexity of the low-carbon CSP system planning and operation collaborative optimization model is effectively reduced, the calculation result precision of the model is ensured, the calculation efficiency can be obviously improved, and the method and the device are suitable for analyzing the long-term planning problem of a large-scale power system, the problems that a traditional optimization model contains binary variables representing the on-off state of a single unit, the model complexity is high, and the calculation efficiency is low are solved.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, a low-carbon CSP system collaborative optimization method based on cluster learning is provided, which comprises the following steps:
performing cluster grouping on CSP units in the low-carbon CSP system to obtain a plurality of CSP unit groups;
constructing output power constraint, climbing constraint, minimum on-line time constraint, minimum off-line time constraint and instantaneous thermal power balance constraint of the CSP unit group by three continuous variables representing the on-line total capacity, the starting total capacity and the stopping total capacity of the CSP unit group, and further constructing a low-carbon CSP system planning and operation collaborative optimization model;
acquiring the rated capacity of each unit in the low-carbon CSP system;
and acquiring a capacity allocation scheme of each unit group according to the rated capacity of each unit and the constructed low-carbon CSP system planning and operation collaborative optimization model.
In a second aspect, a low-carbon CSP system collaborative optimization device based on cluster learning is provided, which includes:
the group division module is used for carrying out cluster grouping on the CSP units in the low-carbon CSP system to obtain a plurality of CSP unit groups;
the model building module is used for building output power constraint, climbing constraint, minimum on-line time constraint, minimum off-line time constraint and instantaneous thermal power balance constraint of the CSP unit group through three continuous variables representing the on-line total capacity, the starting total capacity and the stopping total capacity of the CSP unit group, and further building a low-carbon CSP system planning and operation collaborative optimization model;
the parameter acquisition module is used for acquiring the rated capacity of each unit in the low-carbon CSP system;
and the capacity allocation scheme acquisition module is used for acquiring the capacity allocation scheme of each unit group according to the rated capacity of each unit and the constructed low-carbon CSP system planning and operation collaborative optimization model.
Compared with the prior art, the invention has the beneficial effects that:
when the low-carbon CSP system planning and operation collaborative optimization is carried out, the sets with similar operation characteristics in adjacent geographic areas are clustered, so that the group behavior of the set group is optimized, but not the behavior of a single set, three continuous variables representing the online total capacity, the starting total capacity and the closing total capacity of the CSP set group are introduced to construct each constraint of the CSP set group, and further a low-carbon CSP system planning and operation collaborative optimization model is constructed, because each constraint of the constructed CSP set group does not have a binary variable representing the on-off state of the single set, each constraint of the constructed CSP set group is a complete linear optimization model, the computational complexity of the low-carbon CSP system planning and operation collaborative optimization model is effectively reduced, and the problem that a traditional optimization model comprises a binary variable representing the on-off state of the single set is overcome, the problem of higher model calculation complexity improves the calculation speed, and is suitable for analyzing the long-term planning problem of a large-scale power system.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a flow chart of the method disclosed in example 1;
FIG. 2 is a schematic structural diagram of the CSP unit;
FIG. 3 is a diagram illustrating possible values of the total online capacity of a CSP cluster group.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example 1
In this embodiment, a low-carbon CSP system collaborative optimization method based on cluster learning is disclosed, which includes:
performing cluster grouping on CSP units in the low-carbon CSP system to obtain a plurality of CSP unit groups;
constructing output power constraint, climbing constraint, minimum on-line time constraint, minimum off-line time constraint and instantaneous thermal power balance constraint of the CSP unit group by three continuous variables representing the on-line total capacity, the starting total capacity and the stopping total capacity of the CSP unit group, and further constructing a low-carbon CSP system planning and operation collaborative optimization model;
acquiring the rated capacity of each unit in the low-carbon CSP system;
and acquiring a capacity allocation scheme of each unit group according to the rated capacity of each unit and the constructed low-carbon CSP system planning and operation collaborative optimization model.
Further, the low-carbon CSP system planning and operation collaborative optimization model is constructed by taking the minimum total system cost as a target and taking the power balance constraint, the standby constraint, the low-carbon policy constraint, the output power constraint of the CSP unit group, the climbing constraint, the minimum on-line time constraint, the minimum off-line time constraint, the instantaneous thermal power balance constraint, the charge-discharge balance constraint and the charge state constraint of the heat storage module in the CSP unit as constraint conditions.
Further, the total system cost includes investment costs, fixed operation and maintenance costs, and variable operation costs.
Further, the output power constraint of the constructed CSP unit group is as follows: the output power of each group at the time t is not less than the minimum output power of the group and not more than the maximum output power of the group; the minimum output power and the maximum output power of the CSP unit group are respectively obtained through the ratio of the minimum output power of the group to the online total capacity of the group, the ratio of the maximum output power of the group to the online total capacity of the group and the online total capacity of the group.
Further, the relationship among three continuous variables representing the on-line total capacity, the start-up total capacity and the shut-down total capacity of the CSP unit group is as follows: the difference between the total online capacity of the CSP unit group at the time t and the total online capacity at the time t-1 is equal to the difference between the total startup capacity and the total shutdown capacity of the CSP unit group at the time t.
Furthermore, the online total capacity of the CSP unit group is not less than 0 and not more than the sum of the rated capacities of all CSP units in the group.
Furthermore, the units in the low-carbon CSP system comprise a thermal generator set, a wind generator set, a solar photovoltaic generator set and a CSP unit.
The low-carbon CSP system collaborative optimization method based on cluster learning disclosed in this embodiment is explained in detail.
When a traditional CSP system is planned for a long time, a traditional optimization model is constructed by taking output power constraint, climbing constraint, minimum on-line time constraint, minimum off-line time constraint, instantaneous heat power balance constraint, charge-discharge balance constraint and charge state constraint of a heat storage module in the CSP unit as constraint conditions.
Wherein, the output power constraint of CSP unit is as shown in formula (1):
wherein the content of the first and second substances,I i,t showing the on-off state of the CSP unit i at the time t,
，
representing the output power of the CSP unit i at time t,
and
respectively representing the minimum output power and the maximum output power of the CSP unit iHigh output power.
And
specifically, the following formulas (2) and (3) are shown respectively:
wherein the content of the first and second substances,P i,n represents the rated capacity of the CSP unit i,
and
and respectively representing the ratios of the minimum output power and the maximum output power of the CSP unit i to the rated capacity of the unit.
The climbing constraint of the CSP unit is shown as the formula (4):
wherein the content of the first and second substances,
representing the output power of the CSP unit i at the time t,
indicating CSP unit i is
The output power at the time of day is,
showing the limit of downward climbing of the CSP unit,
showing the limit of upward climbing of the CSP unit.
The minimum online time constraint and the minimum offline time constraint of the CSP unit are respectively shown in formulas (5) and (6):
wherein the content of the first and second substances,I i,t showing the on-off state of the CSP unit i at the time t,I i,t-1 indicating CSP unit i ist-a switching state at time 1,T on represents the minimum online time of the CSP unit,T off and the minimum off-line time of the CSP unit is represented.
The instantaneous thermal power balance constraint of the CSP unit is shown as the formula (7):
wherein the content of the first and second substances,
the output power of the CSP unit at the time t is shown,
representing the charging power of the CSP unit at the time t,
showing the discharge power of the CSP unit at the time t,
the efficiency coefficient of the power module in the CSP unit is shown,
and the solar thermal power available by the CSP unit at the moment t is represented.
The charge-discharge balance constraint of the heat storage module in the CSP unit is as shown in formula (8):
wherein the content of the first and second substances,
the efficiency coefficient of the heat storage module in the CSP unit is shown,E t showing the charge state of the heat storage module in the CSP unit at the time t,E t-1 indicating the heat storage module in CSP unitt-state of charge at time 1.
The charge state constraint of the heat storage module in the CSP unit is as shown in formula (9):
wherein the content of the first and second substances,E minandE maxand respectively representing the lower limit value and the upper limit value of the charge state of the heat storage module in the CSP unit.
It can be known that the constraint conditions of the traditional optimization model include expressions (1) - (9) containing variables representing the on-off state of a single unit, and since the variables representing the on-off state of the single unit have two values, namely 0 and 1, the variables representing the on-off state of the single unit are binary variables, and since the binary variables representing the on-off state of the single unit exist in the traditional optimization model, the traditional optimization model is a mixed integer linear programming model, and when the traditional optimization model is solved, the computation complexity is high, the computation efficiency is low, and the rapid computation of the long-term programming problem of the power system is difficult to realize.
In this embodiment, in order to solve the problems of high computational complexity and low computational efficiency when a conventional optimization model is used for long-term planning of an electric power system, the conventional optimization model is improved, a plurality of CSP unit groups are obtained by performing group division on CSP units in the system, then three continuous variables representing the on-line total capacity, the start-up total capacity and the shut-down total capacity of the CSP unit groups are introduced to construct constraints of the CSP unit groups, on the basis of constructing the constraints of the CSP unit groups, a low-carbon CSP system planning and operation collaborative optimization model is constructed, a capacity configuration scheme of each unit group is obtained by solving the low-carbon CSP system planning and operation collaborative optimization model, because the variables in the constraints of the constructed CSP unit groups are continuous variables, binary variables representing the on-off state of a single unit do not exist, and the model is a complete linear optimization model, therefore, the complexity of the low-carbon CSP system planning and operation collaborative optimization model is effectively reduced, and the efficiency of model calculation is improved.
As shown in fig. 1, the method for collaborative optimization of a low-carbon CSP system based on cluster learning disclosed in this embodiment includes:
s1: and clustering and grouping the CSP units in the low-carbon CSP system to obtain a plurality of CSP unit groups.
The units in the low-carbon CSP system comprise a thermal generator set, a wind generating set, a solar photovoltaic generator set and a CSP unit.
In order to improve the speed of the low-carbon CSP system planning and operation collaborative optimization and reduce the complexity of calculation, when a low-carbon CSP system planning and operation collaborative optimization model is constructed, CSP units are firstly clustered, so that the optimization problem is converted from the behavior of optimizing a single unit to the behavior of optimizing a group, and when the low-carbon CSP system planning and operation collaborative optimization model is specifically implemented, CSP units with similar operation characteristics in adjacent geographic areas are clustered, so that a plurality of CSP unit groups are obtained.
S2: and constructing each constraint of the CSP unit group by three continuous variables representing the on-line total capacity, the starting total capacity and the stopping total capacity of the CSP unit group, and constructing a low-carbon CSP system planning and operation collaborative optimization model according to each constraint of the CSP unit group.
Each constraint of the CSP unit group comprises an output power constraint, a climbing constraint, a minimum on-line time constraint, a minimum off-line time constraint, an instantaneous heat power balance constraint, a charge-discharge balance constraint and a charge state constraint of a heat storage module in the CSP unit group.
The low-carbon CSP system planning and operation collaborative optimization model is specifically constructed as follows: the method is characterized in that the minimum total cost of the system is taken as a target, and the power balance constraint, the standby constraint, the low-carbon policy constraint, the output power constraint of the CSP unit group, the climbing constraint, the minimum on-line time constraint, the minimum off-line time constraint, the instantaneous thermal power balance constraint, the charge-discharge balance constraint and the charge state constraint of the heat storage module in the CSP unit are taken as constraint conditions.
In specific implementation, in order to determine the optimal combination of capacity allocation of each unit and realize the fine and reliable energy supply of the low-carbon CSP system under the condition of environmental constraint, a low-carbon CSP system planning and operation collaborative optimization model is constructed.
The low-carbon CSP system planning and operation collaborative optimization model comprises decision variables of two levels, wherein the decision variables comprise the type of a power generation technology and the new power generation capacity which needs to be invested in a specific year at the planning level, namely the equipment configuration of the system needs to be reasonably designed at the level, and the equipment type selection and the investment capacity determination are included, so that the investment cost is reduced. On the operational level, the decision variables include how much available capacity each power generation technology should invest and schedule at each particular time, i.e., the output of each power generation device is reasonably arranged on this level to achieve economical and reliable operation of the system.
Therefore, the low-carbon CSP system planning and operation collaborative optimization model is constructed by aiming at the minimum total cost of the system, wherein the total cost comprises investment cost, fixed operation and maintenance cost and variable operation cost, and the objective function is shown as the formula (10):
wherein the content of the first and second substances,
in the formula (I), the compound is shown in the specification,Crepresents the total cost;C i which represents the cost of the investment,a th-m 、a w 、a s 、a c-j respectively represents the investment costs of a thermal generator set, a wind generator set, a solar photovoltaic generator set and a CSP set,I th-m 、I w 、I s 、I c-j respectively representing the newly increased capacity of a thermal generator set, a wind generator set, a solar photovoltaic generator set and a CSP set;C f the fixed operation and maintenance cost is shown,f th-m 、f w 、f s 、f c-j respectively represents the fixed operation and maintenance costs of a thermal generator set, a wind generator set, a solar photovoltaic generator set and a CSP set,
、
、
、
respectively indicate the heat powerThe total capacity of the motor set, the wind generating set, the solar photovoltaic generating set and the CSP set;C v represents a variable operating cost, consisting of a start-up cost and a fuel cost, wherein c th-m And SD th-m Respectively representing the fuel cost and the starting cost of the mth type thermal generator set,
the output power of the mth type thermal generator set at the time t is shown,
representing the starting capacity of the mth type thermal generator set at the time t,Mthe category of the thermal generator set is represented,Jthe class of the CSP unit is represented,Twhich represents a time period of time,
representing a time interval.
The CSP unit generally comprises a light-gathering and heat-collecting module, a heat storage module and a power generation module, as shown in fig. 2. The light-gathering and heat-collecting module gathers solar energy to the solar energy collecting device through the reflector, and further heats the heat-conducting working medium in the solar energy collecting device, so that the solar energy is converted into heat energy; the heat-conducting working medium flows into the heat storage module to carry out heat exchange, so that heat storage or heat release can be realized; the power generation module can convert heat energy into mechanical energy and finally into electric energy through a turbine generator.
In order to improve the speed of the low-carbon CSP system planning and operation collaborative optimization and reduce the complexity of calculation, when a low-carbon CSP system planning and operation collaborative optimization model is constructed, CSP units are grouped in a cluster manner, so that the optimization problem is converted from the behavior of optimizing a single unit to the behavior of optimizing the group, and three continuous variables representing the online total capacity, the starting total capacity and the stopping total capacity of the CSP unit group are introduced to construct the constraint condition of the low-carbon CSP system planning and operation collaborative optimization model.
The constraint conditions of the low-carbon CSP system planning and operation collaborative optimization model comprise: the system comprises a system power balance constraint, a standby constraint, a low-carbon policy constraint, an output power constraint of a CSP unit group, a climbing constraint, a minimum on-line time constraint, a minimum off-line time constraint, an instantaneous heat power balance constraint, a charge-discharge balance constraint and a charge state constraint of a heat storage module in the CSP unit and the like.
The primary task of the operation of the power system is to ensure the safe and stable operation of the system, so the low-carbon CSP system planning and operation collaborative optimization model disclosed in this embodiment must satisfy the power balance constraint of the power system, that is, the sum of the generated powers of the thermal generator set, the wind generator set, the solar photovoltaic generator set and the CSP unit should always be equal to the sum of the power demand of the local area and the power transmitted to the outside of the area, as shown in formula (14):
wherein the content of the first and second substances,D t indicating the power demand of the region at t hours,
indicating the power value that the region transmits outside the region in t hours,
and the output powers of the thermal generator set, the wind generator set, the solar photovoltaic generator set and the CSP set group in t hours are respectively represented.
For a thermal generator set, the hourly output power of the thermal generator set should not exceed the total installed capacity, as shown in equation (15):
wherein the content of the first and second substances,
and
respectively representing the output power and the online capacity of the mth type thermal generator set in t hours,
and
and respectively representing the total installed capacity, the existing capacity and the newly added capacity of the mth type of thermal generator set.
For a wind generating set, a solar photovoltaic generating set and a CSP set, the hourly output power of the wind generating set, the solar photovoltaic generating set and the CSP set is limited by the existing capacity, the newly-built capacity and the continuously-changed capacity factor, and the output power is respectively shown as formulas (16), (17) and (18):
wherein the content of the first and second substances,
respectively represents the output power of the wind generating set, the solar photovoltaic generating set and the CSP set group in t hours,
respectively represents the hourly capacity factors of the wind generating set, the solar photovoltaic generating set and the CSP set group in t hours,
respectively representing the total capacity of the wind generating set, the solar photovoltaic generating set and the CSP set group，
Respectively represents the existing capacities of a wind generating set, a solar photovoltaic generating set and a CSP set group,
respectively showing the newly added capacities of the wind generating set, the solar photovoltaic generating set and the CSP set group.
In order to ensure safe and reliable operation of the power system, a certain margin is often required to be reserved to deal with system emergency situations when power supply planning is performed. In the embodiment, the prediction errors of the wind energy and the solar energy output power are considered, and the uncertainty of wind power generation and photovoltaic power generation is converted into the spare capacity of the system, so that the economic and stable operation of the power system is ensured. Considering the randomness and the volatility of wind and light power generation, the standby constraint of the construction system is shown as the formula (19):
wherein the content of the first and second substances,
represents the maximum output ratio of the mth type thermal generator set at the time t,
represents a backup requirement related to the power demand at time t, which is equal to the installed capacity of the largest thermal generator set in the area or the expected load deviation due to prediction error,
、
and respectively representing the prediction errors of the output power of the wind generating set, the solar photovoltaic generating set and the CSP set.
The Renewable energy investment Portfolio Standard (RPS) requires that a power provider must have a lowest Renewable energy ratio, and the embodiment is intended to implement low-carbon policy constraint by using the RPS Standard, as shown in formula (20):
wherein the content of the first and second substances,rrepresenting the proportion of the renewable energy power generation in the total power generation.
Each constraint that constructs a CSP unit group by three continuous variables that respectively represent the on-line total capacity, the start-up total capacity, and the shut-down total capacity of the CSP unit group is explained in detail.
Each constraint of the CSP unit group comprises an output power constraint, a climbing constraint, a minimum on-line time constraint, a minimum off-line time constraint, an instantaneous heat power balance constraint, a charge-discharge balance constraint and a charge state constraint of a heat storage module in the CSP unit group.
In order to ensure that all variables in each constraint of the constructed CSP unit group are continuous and binary variables representing the on-off state of the CSP unit do not exist, so that each constraint of the constructed CSP unit group is a complete linear optimization model, the complexity of a low-carbon CSP system planning and operation collaborative optimization model is reduced, and the calculation efficiency of the model is improved, firstly, integer variables respectively representing the online total capacity, the starting total capacity and the shutdown total capacity of the CSP unit group are introduced into a traditional optimization model
、
、
And simulating the group behaviors of all the units in the group, as shown in formulas (21), (22) and (23):
wherein the content of the first and second substances,
the online total capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units which are running in the CSP unit group at the time t,I i,t indicating the switching state of the CSP unit and, when the unit is operating,I i,t =1, and otherwise,I i,t =0；
the starting total capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units started in the CSP unit group at the time t,u i,t indicating the start-up status of the CSP unit and, when the unit is started,u i,t =1, and otherwise,u i,t =0；
representing the shutdown total capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units shutdown in the CSP unit group j at the time t,d i,t indicating the shutdown state of the CSP unit, when the unit is shut down,d i,t =1, and otherwise,d i,t =0；P i,n represents the rated capacity of the CSP unit i,Ithe number of sets in the group is shown. It should be noted that when passing through
、
、
In constructing the various constraints of a CSP fleet,
、
、
all indirect control variables have integer characteristics, all discrete values are taken, so that the integer variables still exist in various constraints of the constructed CSP unit group, when an optimization model is constructed through the constraint conditions to plan the power system,
is given a possible value ofI i,t Different combinations of (a) and (b). For example, in a group of 10 CSP units, if the rated capacity of each CSP unit is not the same, then
There are at most 1024 possible values, which can be reduced by assuming that the rated capacity of all units in the group is the same
The property of mixed integers of various constraints of the constructed CSP unit group cannot be changed, so that the optimization model constructed by the various constraints of the CSP unit group still has higher complexity and lower calculation efficiency.
This example is introduced
、
、
Based on the three integer variables, by continuous variables
、
And
to approximate respectively to integer variables
、
And
and further using continuous variables
、
And
to replace integer variables
、
And
building the constraints of the final CSP unit groupAll variables in each constraint of the finally constructed CSP unit group are continuous and do not contain 3 x representing the on-off state of each unit in the traditional optimization modelIThe binary variables are complete linear optimization models, so that the number of decision variables is greatly reduced, the complexity of a low-carbon CSP system planning and operation collaborative optimization model is reduced, and the calculation speed of the model is accelerated.
Wherein the continuous variable
The online total capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units which are running in the CSP unit group at the time t j, satisfies the formula (24):
wherein the content of the first and second substances,S j the total capacity of the CSP unit group j is represented, namely the sum of the rated capacities of all CSP units in the group j is obtained by the formula (25):
wherein the content of the first and second substances,
and representing the maximum output power of the CSP unit i in the j group.
Continuous variable
Representing the total starting capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units started in the group at the time t and a continuous variable
And (3) representing the shutdown total capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units shutdown in the CSP unit group j at the time t.
Continuous variable
、
And
the relationship therebetween conforms to equation (26):
based on the continuous decision variables, the output power constraint of the CSP unit group is as the following formula (27):
wherein the content of the first and second substances,P j,min andP j,max the minimum output power and the maximum output power of the CSP unit group j are respectively represented by equations (28) and (29):
wherein the content of the first and second substances,
and
and respectively representing the ratios of the minimum output power and the maximum output power of the CSP unit group j to the online total capacity of the CSP unit group j. For a group of units with similar operating characteristics,
and
、
and
the difference between them is relatively small, so take
=
，
=
。
Based on the continuous decision variables, the climbing constraints of the CSP unit group are represented by the formulas (30) and (31):
wherein the content of the first and second substances,
and
respectively representing the upward and downward climbing rates.
Supplementing equations (30) and (31), further adding a constraint condition to the output power of the CSP unit group j at time t, as shown in equation (32):
based on the continuous decision variables, the minimum online time constraint and the minimum offline time constraint of the CSP unit group are expressed as formulas (33), (34), (35) and (36):
the instantaneous thermal power balance constraint of a CSP cluster is represented by equation (37):
because the decision variables in the charge-discharge balance constraint of the heat storage module of the CSP unit and the charge state constraint of the heat storage module in the traditional optimization model are continuous variables, the charge-discharge balance constraint and the charge state constraint of the heat storage module in the CSP unit of the low-carbon CSP system planning and operation collaborative optimization model still adopt the formulas (8) and (9).
Therefore, the output power constraint, the climbing constraint, the minimum on-line time constraint, the minimum off-line time constraint, the instantaneous heat power balance constraint, the charge-discharge balance constraint and the charge state constraint of the heat storage module in the CSP unit in the low-carbon CSP system planning and operation collaborative optimization model comprise the formulas (8), (9) and (24) - (37), wherein all variables are continuous, do not relate to binary variables representing the switch state of the CSP unit, are complete linear optimization models, the complexity of the low-carbon CSP system planning and operation collaborative optimization model is reduced, when the low-carbon CSP system planning and operation collaborative optimization model is used for long-term planning of the low-carbon CSP-containing system, the calculation result precision is maintained, the calculation efficiency can be obviously improved, and the problem that the traditional optimization model contains the binary variables representing the switch state of the CSP unit is solved, high model complexity and low calculation efficiency.
S3: and obtaining the rated capacity of each unit in the low-carbon CSP system.
S4: and obtaining a capacity allocation scheme of each unit group according to the rated capacity of each unit and the constructed low-carbon CSP system planning and operation collaborative optimization model.
To further illustrate the feasibility and effectiveness of the proposed method of the present invention, examples are given. For example, for a CSP unit group comprising 5 CSP units, wherein the rated capacity of each unit is 250, 300, 330, 350, 350MW, if the traditional optimization model is adopted, each CSP unit exists on line (I i,t = 1) and offline (I i,t = 0), then an integer variable representing the total online capacity of the entire fleet group (i.e. 5 CSP fleets)
There may be twenty-four possible values of 0, 250, 300, 330, 350, 550, 580, 600, 630, 650, 680, 700, 880, 900, 930, 950, 980, 1000, 1030, 1230, 1250, 1280, 1330, 1580, as in FIG. 3 "
"is shown; if the traditional clustering method is adopted for simplification, the rated capacity of each CSP unit is assumed to be 316MW (average value), and the whole unit is representedInteger variable of online total capacity of group (i.e. 5 CSP units)
Can take six possible values of 0, 316, 632, 948, 1264 and 1580, as shown in figure 3 "
"so that the decision variables are reduced in this way
But does not change the mixed integer nature of the constraints being built, resulting in a computation speed that is still slow when solving the model. The low-carbon CSP system collaborative optimization method based on cluster learning introduces continuous variable representing the online total capacity of the whole unit group (namely 5 CSP units)
Since the total capacity of the whole unit group, namely the sum of the rated capacities of 5 CSP units is equal toS j =1580MW, therefore
Can take any value between 0 and 1580MW, as in FIG. 3 "
"show, if the optimal value of the online total capacity obtained by optimization is 750MW, the actual value should be 880MW, and the difference between the two is 130MW, which only occupies the total capacityS j And 8.2 percent of the total number of the units in the group, wherein the maximum difference is not larger than the rated capacity of the maximum unit in the group, and the difference is reduced along with the increase of the number of the units in the group, so the method can remarkably reduce the calculation complexity while maintaining the accuracy of the calculation result.
Therefore, the low-carbon CSP system collaborative optimization method based on cluster learning disclosed by this embodiment establishes a more detailed low-carbon CSP system planning and operation collaborative optimization model by introducing three continuous variables representing the on-line total capacity, the start total capacity and the stop total capacity of the CSP unit group, and each constraint of the CSP unit group in the established low-carbon CSP system planning and operation collaborative optimization model does not contain a binary variable representing the on-off state of a single unit, so that the low-carbon CSP system collaborative optimization method is a complete linear optimization model, reduces the complexity of model solution, improves the calculation efficiency, and is more suitable for planning and analysis of a large-scale power system in a diversified complex scene at a long time scale.
Example 2
In this embodiment, a low-carbon CSP system cooperative optimization device based on cluster learning is disclosed, which includes:
the group division module is used for carrying out cluster grouping on the CSP units in the low-carbon CSP system to obtain a plurality of CSP unit groups;
the model building module is used for building output power constraint, climbing constraint, minimum on-line time constraint, minimum off-line time constraint and instantaneous thermal power balance constraint of the CSP unit group through three continuous variables representing the on-line total capacity, the starting total capacity and the stopping total capacity of the CSP unit group, and further building a low-carbon CSP system planning and operation collaborative optimization model;
the parameter acquisition module is used for acquiring the rated capacity of each unit in the low-carbon CSP system;
and the capacity allocation scheme acquisition module is used for acquiring the capacity allocation scheme of each unit group according to the rated capacity of each unit and the constructed low-carbon CSP system planning and operation collaborative optimization model.
It should be noted that specific implementation manners of the modules are already described in detail in embodiment 1, and are not described again.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (2)
1. The low-carbon CSP system collaborative optimization method based on cluster learning is characterized by comprising the following steps:
performing cluster grouping on CSP units in the low-carbon CSP system to obtain a plurality of CSP unit groups;
constructing output power constraint, climbing constraint, minimum on-line time constraint, minimum off-line time constraint and instantaneous thermal power balance constraint of the CSP unit group by three continuous variables representing the on-line total capacity, the starting total capacity and the stopping total capacity of the CSP unit group, and further constructing a low-carbon CSP system planning and operation collaborative optimization model;
acquiring the rated capacity of each unit in the low-carbon CSP system;
acquiring a capacity allocation scheme of each unit group according to the rated capacity of each unit and the constructed low-carbon CSP system planning and operation collaborative optimization model;
the low-carbon CSP system planning and operation collaborative optimization model is constructed by taking the minimum total cost of the system as a target, wherein the total cost comprises investment cost, fixed operation and maintenance cost and variable operation cost, and the target function is as follows:
wherein the content of the first and second substances,
in the formula (I), the compound is shown in the specification,Crepresents the total cost;C i which represents the cost of the investment,a th-m 、a w 、a s 、a c-j respectively represents the investment costs of a thermal generator set, a wind generator set, a solar photovoltaic generator set and a CSP set,I th-m 、I w 、I s 、I c-j respectively representing the newly increased capacity of a thermal generator set, a wind generator set, a solar photovoltaic generator set and a CSP set;C f the fixed operation and maintenance cost is shown,f th-m 、f w 、f s 、f c-j respectively represents the fixed operation and maintenance costs of a thermal generator set, a wind generator set, a solar photovoltaic generator set and a CSP set,
、
、
、
respectively representing the total capacity of the thermal generator set, the wind generator set, the solar photovoltaic generator set and the CSP set;C v represents a variable operating cost, consisting of a start-up cost and a fuel cost, wherein c th-m And SD th-m Respectively representing the fuel cost and the starting cost of the mth type thermal generator set,
the output power of the mth type thermal generator set at the time t is shown,
representing the starting capacity of the mth type thermal generator set at the time t,Mthe category of the thermal generator set is represented,Jthe class of the CSP unit is represented,Twhich represents a time period of time,
represents a time interval; the low-carbon CSP system planning and operation collaborative optimization model must meet the power balance constraint of the power system, namely the sum of the generated energy of the thermal generator set, the wind generator set, the solar photovoltaic generator set and the CSP unit should always be equal to the sum of the power demand of the region and the power transmitted outside the region, as follows:
wherein the content of the first and second substances,D t indicating the power demand of the region at t hours,
indicating the power value that the region transmits outside the region in t hours,
respectively representing the output power of a thermal generator set, a wind generator set, a solar photovoltaic generator set and a CSP set group in t hours;
for a thermal power generating unit, the hourly output power of the thermal power generating unit should not exceed the total installed capacity, as follows:
wherein the content of the first and second substances,
and
respectively representing the output power and the online capacity of the mth type thermal generator set in t hours,
and
respectively representing the total installed capacity, the existing capacity and the newly added capacity of the mth type of thermal generator set;
for a wind generating set, a solar photovoltaic generating set and a CSP set, the hourly output power is limited by the existing capacity, the newly-built capacity and the continuously-changed capacity factor, which are respectively as follows:
wherein the content of the first and second substances,
respectively represents the output power of the wind generating set, the solar photovoltaic generating set and the CSP set group in t hours,
respectively represents the hourly capacity factors of the wind generating set, the solar photovoltaic generating set and the CSP set group in t hours,
respectively represents the total capacity of the wind generating set, the solar photovoltaic generating set and the CSP set group,
respectively represents the existing capacities of a wind generating set, a solar photovoltaic generating set and a CSP set group,
respectively representing the newly added capacities of the wind generating set, the solar photovoltaic generating set and the CSP set group;
considering the randomness and the volatility of the wind-solar power generation, the standby constraint for constructing the system is as follows:
wherein the content of the first and second substances,
represents the maximum output ratio of the mth type thermal generator set at the time t,
represents a backup requirement related to the power demand at time t, which is equal to the installed capacity of the largest thermal generator set in the area or the expected load deviation due to prediction error,
、
respectively representing the prediction errors of the output power of the wind generating set, the solar photovoltaic generating set and the CSP set;
the renewable energy investment portfolio standard requires that a power supplier must have a lowest renewable energy proportion, and adopts the RPS standard to realize low-carbon policy constraint:
wherein the content of the first and second substances,rrepresenting the proportion of the renewable energy power generation in the total power generation;
integer variables respectively representing the on-line total capacity, the starting total capacity and the shutdown total capacity of the CSP unit group are introduced into a traditional optimization model
、
、
To simulate the group behavior of all units in the group, as follows:
wherein the content of the first and second substances,
the online total capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units which are running in the CSP unit group at the time t,I i,t indicating the switching state of the CSP unit and, when the unit is operating,I i,t =1, and otherwise,I i,t =0；
the starting total capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units started in the CSP unit group at the time t,u i,t indicating the start-up status of the CSP unit and, when the unit is started,u i,t =1, and otherwise,u i,t =0；
representing the shutdown total capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units shutdown in the CSP unit group j at the time t,d i,t indicating the shutdown state of the CSP unit, when the unit is shut down,d i,t =1, and otherwise,d i,t =0；P i,n represents the rated capacity of the CSP unit i,Ithe number of groups in the group; when passing through
、
、
In constructing the various constraints of a CSP fleet,
、
、
all indirect control variables have integer characteristics, and all discrete values are taken, so that the integer variables still exist in various constraints of the constructed CSP unit group; in the introduction of
、
、
Based on the three integer variables, by continuous variables
、
And
to approximate respectively to integer variables
、
And
and further using continuous variables
、
And
to replace integer variables
、
And
and constructing each constraint of the final CSP unit group, so that all variables in each constraint of the finally constructed CSP unit group are continuous and do not contain 3 x representing the on-off state of each unit in the traditional optimization modelIA binary variable which is a complete linear optimization model;
wherein the continuous variable
The online total capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units running in the CSP unit group at the time t j, satisfies the following conditions:
wherein the content of the first and second substances,S j the total capacity of the CSP unit group j is represented, namely the sum of the rated capacities of all CSP units in the group j is obtained according to the following formula:
in the formula (I), the compound is shown in the specification,
representing the maximum output power of the CSP unit i in the j group;
continuous variable
Representing the total starting capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units started in the group at the time t and a continuous variable
Representing the shutdown total capacity of the CSP unit group j at the time t, namely the sum of the rated capacities of the CSP units shutdown in the CSP unit group j at the time t;
continuous variable
、
And
the relationship between them follows the formula:
based on the continuous decision variables, the output power constraint of the CSP unit group is as follows:
wherein the content of the first and second substances,P j,min andP j,max respectively representing the minimum output power and the maximum output power of the CSP unit group j, and respectively obtaining the minimum output power and the maximum output power through the following formulas:
wherein the content of the first and second substances,
and
respectively representing the ratios of the minimum output power and the maximum output power of the CSP unit group j to the on-line total capacity of the CSP unit group j, for a group of units with similar operating characteristics,
and
、
and
the difference between them is relatively small, so take
=
，
=
；
Based on the continuous decision variables, the climbing constraint of the CSP unit group is as follows:
wherein the content of the first and second substances,
and
respectively representing the upward climbing rate and the downward climbing rate;
and further adding a constraint condition to the output power of the CSP unit group j at the time t, wherein the constraint condition is as follows:
based on the continuous decision variables, the minimum online time constraint and the minimum offline time constraint of the CSP unit group are as follows:
the instantaneous thermal power balance constraints of a CSP unit group are as follows:
wherein the content of the first and second substances,
the efficiency coefficient of the power module in the CSP unit is shown,
representing the charging power of the CSP unit at the time t,
showing the discharge power of the CSP unit at the time t,
the available solar thermal power of the CSP unit at the moment t is represented;
because the decision variables in the charge-discharge balance constraint of the heat storage module of the CSP unit and the charge state constraint of the heat storage module in the traditional optimization model are continuous variables, the charge-discharge balance constraint and the charge state constraint of the heat storage module in the CSP unit of the low-carbon CSP system planning and operation collaborative optimization model still adopt the following formulas:
and (3) charge-discharge balance constraint of the heat storage module in the CSP unit:
wherein the content of the first and second substances,
the efficiency coefficient of the heat storage module in the CSP unit is shown,E t showing the charge state of the heat storage module in the CSP unit at the time t,E t-1 indicating the heat storage module in CSP unitt-state of charge at time 1;
and (3) charge state constraint of a heat storage module in the CSP unit:
wherein the content of the first and second substances,E minandE maxrespectively representing CSP unitsAnd the lower limit value and the upper limit value of the state of charge of the intermediate heat storage module.
2. Low carbon CSP system collaborative optimization device based on cluster learning, its characterized in that includes:
the group division module is used for carrying out cluster grouping on the CSP units in the low-carbon CSP system to obtain a plurality of CSP unit groups;
the model building module is used for building output power constraint, climbing constraint, minimum on-line time constraint, minimum off-line time constraint and instantaneous thermal power balance constraint of the CSP unit group through three continuous variables representing the on-line total capacity, the starting total capacity and the stopping total capacity of the CSP unit group, and further building a low-carbon CSP system planning and operation collaborative optimization model;
the parameter acquisition module is used for acquiring the rated capacity of each unit in the low-carbon CSP system;
the capacity allocation scheme acquisition module is used for acquiring the capacity allocation scheme of each unit group according to the rated capacity of each unit and the constructed low-carbon CSP system planning and operation collaborative optimization model;
the low-carbon CSP system planning and operation collaborative optimization model is constructed by taking the minimum total cost of the system as a target, wherein the total cost comprises investment cost, fixed operation and maintenance cost and variable operation cost, and the target function is as follows:
wherein the content of the first and second substances,
in the formula (I), the compound is shown in the specification,Crepresents the total cost;C i which represents the cost of the investment,a th-m 、a w 、a s 、a c-j respectively represents the investment costs of a thermal generator set, a wind generator set, a solar photovoltaic generator set and a CSP set,I th-m 、I w 、I s 、I c-j respectively representing the newly increased capacity of a thermal generator set, a wind generator set, a solar photovoltaic generator set and a CSP set;C f the fixed operation and maintenance cost is shown,f th-m 、f w 、f s 、f c-j respectively represents the fixed operation and maintenance costs of a thermal generator set, a wind generator set, a solar photovoltaic generator set and a CSP set,
、
、
、
respectively representing the total capacity of the thermal generator set, the wind generator set, the solar photovoltaic generator set and the CSP set;C v represents a variable operating cost, consisting of a start-up cost and a fuel cost, wherein c th-m And SD th-m Respectively representing the fuel cost and the starting cost of the mth type thermal generator set,
expressing the m-th class of fireThe output power of the generator set at the time t,
representing the starting capacity of the mth type thermal generator set at the time t,Mthe category of the thermal generator set is represented,Jthe class of the CSP unit is represented,Twhich represents a time period of time,
represents a time interval;
the low-carbon CSP system planning and operation collaborative optimization model must meet the power balance constraint of the power system, namely the sum of the generated energy of the thermal generator set, the wind generator set, the solar photovoltaic generator set and the CSP unit should always be equal to the sum of the power demand of the region and the power transmitted outside the region, as follows:
wherein the content of the first and second substances,D t indicating the power demand of the region at t hours,
indicating the power value that the region transmits outside the region in t hours,
respectively representing the output power of a thermal generator set, a wind generator set, a solar photovoltaic generator set and a CSP set group in t hours;
for a thermal power generating unit, the hourly output power of the thermal power generating unit should not exceed the total installed capacity, as follows:
wherein the content of the first and second substances,
and
respectively representing the output power and the online capacity of the mth type thermal generator set in t hours,
and
respectively representing the total installed capacity, the existing capacity and the newly added capacity of the mth type of thermal generator set;
for a wind generating set, a solar photovoltaic generating set and a CSP set, the hourly output power is limited by the existing capacity, the newly-built capacity and the continuously-changed capacity factor, which are respectively as follows:
wherein the content of the first and second substances,
respectively represents the output power of the wind generating set, the solar photovoltaic generating set and the CSP set group in t hours,
respectively represent a wind generating set, a solar photovoltaic generating set and a CSThe hourly capacity factor of the P unit group in t hours,
respectively represents the total capacity of the wind generating set, the solar photovoltaic generating set and the CSP set group,
respectively represents the existing capacities of a wind generating set, a solar photovoltaic generating set and a CSP set group,
respectively representing the newly added capacities of the wind generating set, the solar photovoltaic generating set and the CSP set group;
considering the randomness and the volatility of the wind-solar power generation, the standby constraint for constructing the system is as follows:
wherein the content of the first and second substances,
represents the maximum output ratio of the mth type thermal generator set at the time t,
represents a backup requirement related to the power demand at time t, which is equal to the installed capacity of the largest thermal generator set in the area or the expected load deviation due to prediction error,
、
respectively representing the output power prediction errors of the wind generating set, the solar photovoltaic generating set and the CSP setA difference;
the renewable energy investment portfolio standard requires that a power supplier must have a lowest renewable energy proportion, and adopts the RPS standard to realize low-carbon policy constraint:
wherein the content of the first and second substances,rrepresenting the proportion of the renewable energy power generation in the total power generation;
integer variables respectively representing the on-line total capacity, the starting total capacity and the shutdown total capacity of the CSP unit group are introduced into a traditional optimization model
、
、
To simulate the group behavior of all units in the group, as follows:
wherein the content of the first and second substances,
indicating the total online capacity of the CSP unit group j at time t, i.e. the nominal capacity of the CSP units operating in the group at time tThe sum of the capacities is,I i,t indicating the switching state of the CSP unit and, when the unit is operating,I i,t =1, and otherwise,I i,t =0；
the starting total capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units started in the CSP unit group at the time t,u i,t indicating the start-up status of the CSP unit and, when the unit is started,u i,t =1, and otherwise,u i,t =0；
representing the shutdown total capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units shutdown in the CSP unit group j at the time t,d i,t indicating the shutdown state of the CSP unit, when the unit is shut down,d i,t =1, and otherwise,d i,t =0；P i,n represents the rated capacity of the CSP unit i,Ithe number of groups in the group; when passing through
、
、
In constructing the various constraints of a CSP fleet,
、
、
all indirect control variables have integer characteristics, and all discrete values are taken, so that the integer variables still exist in various constraints of the constructed CSP unit group; in the introduction of
、
、
Based on the three integer variables, by continuous variables
、
And
to approximate respectively to integer variables
、
And
and further using continuous variables
、
And
to replace integer variables
、
And
and constructing each constraint of the final CSP unit group, so that all variables in each constraint of the finally constructed CSP unit group are continuous and do not contain 3 x representing the on-off state of each unit in the traditional optimization modelIA binary variable which is a complete linear optimization model;
wherein the continuous variable
The online total capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units running in the CSP unit group at the time t j, satisfies the following conditions:
wherein the content of the first and second substances,S j the total capacity of the CSP unit group j is represented, namely the sum of the rated capacities of all CSP units in the group j is obtained according to the following formula:
in the formula (I), the compound is shown in the specification,
representing the maximum output power of the CSP unit i in the j group;
continuous variable
Representing the total starting capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units started in the group at the time t and a continuous variable
Representing the shutdown total capacity of the CSP unit group j at the time t, namely the sum of the rated capacities of the CSP units shutdown in the CSP unit group j at the time t;
continuous variable
、
And
the relationship between them follows the formula:
based on the continuous decision variables, the output power constraint of the CSP unit group is as follows:
wherein the content of the first and second substances,P j,min andP j,max respectively representing the minimum output power and the maximum output power of the CSP unit group j, and respectively obtaining the minimum output power and the maximum output power through the following formulas:
wherein the content of the first and second substances,
and
respectively representing the ratios of the minimum output power and the maximum output power of the CSP unit group j to the on-line total capacity of the CSP unit group j, for a group of units with similar operating characteristics,
and
、
and
the difference between them is relatively small, so take
=
，
=
；
Based on the continuous decision variables, the climbing constraint of the CSP unit group is as follows:
wherein the content of the first and second substances,
and
respectively representing the upward climbing rate and the downward climbing rate;
and further adding a constraint condition to the output power of the CSP unit group j at the time t, wherein the constraint condition is as follows:
based on the continuous decision variables, the minimum online time constraint and the minimum offline time constraint of the CSP unit group are as follows:
the instantaneous thermal power balance constraints of a CSP unit group are as follows:
wherein the content of the first and second substances,
the efficiency coefficient of the power module in the CSP unit is shown,
representing the charging power of the CSP unit at the time t,
showing the discharge power of the CSP unit at the time t,
the available solar thermal power of the CSP unit at the moment t is represented;
because the decision variables in the charge-discharge balance constraint of the heat storage module of the CSP unit and the charge state constraint of the heat storage module in the traditional optimization model are continuous variables, the charge-discharge balance constraint and the charge state constraint of the heat storage module in the CSP unit of the low-carbon CSP system planning and operation collaborative optimization model still adopt the following formulas:
and (3) charge-discharge balance constraint of the heat storage module in the CSP unit:
wherein the content of the first and second substances,
the efficiency coefficient of the heat storage module in the CSP unit is shown,E t showing the charge state of the heat storage module in the CSP unit at the time t,E t-1 indicating the heat storage module in CSP unitt-state of charge at time 1;
and (3) charge state constraint of a heat storage module in the CSP unit:
wherein the content of the first and second substances,E minandE maxand respectively representing the lower limit value and the upper limit value of the charge state of the heat storage module in the CSP unit.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102170170A (en) * | 2011-04-02 | 2011-08-31 | 清华大学 | Wind-power adsorption connected large-power-grid scheduling rolling planning method |
| US20140116048A1 (en) * | 2012-10-29 | 2014-05-01 | Institute of Nuclear Energy Research, Atomic Energy Council, Executive Yuan, R.O.C. | Multi-Functional Solar Combined Heat and Power System |
| CN105870976A (en) * | 2016-04-15 | 2016-08-17 | 国家电网公司 | Energy environment efficiency-based low-carbon dispatching method and device |
| CN106548413A (en) * | 2016-10-18 | 2017-03-29 | 中国电力科学研究院 | A kind of power system energy storage fitness-for-service assessment method and system |
| CN107591830A (en) * | 2017-10-23 | 2018-01-16 | 国网能源研究院有限公司 | A kind of electricity power planing method for being suitable to Renewable Energy Development at high proportion |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP7241644B2 (en) * | 2019-08-29 | 2023-03-17 | 三菱電機株式会社 | Power plant operation planning device and power plant operation planning method |
| JP7313300B2 (en) * | 2020-02-26 | 2023-07-24 | 株式会社日立製作所 | Distributed resource management device and distributed resource management method |
-
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102170170A (en) * | 2011-04-02 | 2011-08-31 | 清华大学 | Wind-power adsorption connected large-power-grid scheduling rolling planning method |
| US20140116048A1 (en) * | 2012-10-29 | 2014-05-01 | Institute of Nuclear Energy Research, Atomic Energy Council, Executive Yuan, R.O.C. | Multi-Functional Solar Combined Heat and Power System |
| CN105870976A (en) * | 2016-04-15 | 2016-08-17 | 国家电网公司 | Energy environment efficiency-based low-carbon dispatching method and device |
| CN106548413A (en) * | 2016-10-18 | 2017-03-29 | 中国电力科学研究院 | A kind of power system energy storage fitness-for-service assessment method and system |
| CN107591830A (en) * | 2017-10-23 | 2018-01-16 | 国网能源研究院有限公司 | A kind of electricity power planing method for being suitable to Renewable Energy Development at high proportion |
Non-Patent Citations (6)
| Title |
|---|
| Coordinated optimization scheduling of wind power, photovoltaic, photo-thermal and hydropower considering the schedulability of photo-thermal power stations;Hou Bingchen;《2019 International Conference on Intelligent Green Building and Smart Grid (IGBSG2019)》;20191031;全文 * |
| Distributed Piecewise Approximation Economic Dispatch for Regional Power Systems Under Non-Ideal Communication;RAN HAO;《IEEE ACCESS》;20190416;全文 * |
| The Capacity Joint Optimization of Energy Storage and Renewable Generation Based on Simulation;Zhaodi Shi;《2018 International Conference on Power System Technology (POWERCON)》;20190107;全文 * |
| 含多微网的主动配电***综合优化运行行为分析与建模;吕天光;《中国电机工程学报》;20160105;第36卷(第1期);全文 * |
| 考虑需求响应的含光热电站可再生能源高渗透率电力***多源优化调度;崔杨;《高电压技术》;20200531;第46卷(第5期);全文 * |
| 高比例清洁能源并网的跨国互联电力***多时间尺度储能容量优化规划;姜海洋;《中国电机工程学报》;20210320;第41卷(第6期);全文 * |
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